Even if it’s not the buzzword it was a few years ago, the concept of big data — unstructured information that was increasing in velocity, volume and variety — caused many companies to think about how it could change the way they operate. That said, a lot of the biggest use cases vendors, consultants and other experts talked about tended to focus on the potential to harness big data in business-to-consumer (B2C) context.
Today, you may be working in an organization where talk of big data has shifted to other technologies, including artificial intelligence, blockchain or the Internet of Things. Those are all areas of innovation that may be well-worth pursuing. A lot of those technologies, however — and many others beside them — would still benefit from a strategic use of big data. This includes B2B firms who have the unique challenge of marketing and selling to other enterprises.
Here are four examples worth bringing forward to your team, whether you work in IT, marketing or even sales:
1. Helping to Create Better B2B Loyalty Programs
B2C loyalty programs are common methods of increasing customer engagement. However, they’re becoming more readily available in the B2B market, too. One thing that differentiates the B2B market from B2C is that numerous people at an organization may act as decision-makers. Therefore, creating a B2B loyalty program could be comparatively more complicated due to figuring out how to entice multiple people within an organization.
In one test of using big data models for B2B loyalty programs, the people involved determined that multiple things constitute loyalty. It also found that big data models could help determine the entities most likely to show future commitment. Then, a B2B organization could focus on those clients when tweaking its loyalty program offerings.
Some of the components of an effective B2B loyalty program in 2019 include referral program options and giving instant discounts based on transactions. Big data platforms can help B2B companies narrow down their choices of rewards and opportunities to offer, thereby increasing the outcomes of a B2B program.
2. Increasing the Payoffs of B2B Trade Shows
B2B trade shows are useful for connecting providers with people who are ready to buy. However, regardless of how many leads B2B companies find at a given trade show, they have to follow up with those parties after the event ends. Considering the number of people a B2B enterprise may encounter at a single show, it’s crucial to categorize those contacts for better lead generation. Big data can help with that.
If a potential lead fills out a form at a trade show that asks things like the company size, average revenue and number of employees, a big data platform could receive such information and determine which characteristics most likely indicate high-value leads. It then becomes possible to turn more attention toward them, making trade show appearances more lucrative.
Of course, big data assists with B2B sales outside of trade shows, too. Due to the information that analytics platforms give, marketing professionals can evaluate which contacts are most likely to convert to leads after providing basic details to sign up for a newsletter, for example.
3. Determining the Content Types That B2B Companies Want Most
Content marketing is an essential ingredient for interacting with potential customers and spurring sales. According to the B2B Content Marketing 2019 report that identifies North American trends, 72% of the most successful B2B content marketers measure return on investment (ROI). Also, analytics tools ranked as the third-most technology used for content marketing efforts.
If a B2B company representative keeps those two statistics in mind, they could conclude that big data allows them to investigate which types of content delivered the highest ROI for their organization. In that case, it becomes easier to allocate the marketing budget in well-informed ways that push the B2B company forward.
Similarly, big data analytics could show how customer engagement with content differs as a person moves through the sales funnel. For example, a B2B customer likely won’t engage with a large content asset, such as a buying guide or a white paper, until they are at a sufficiently late stage in their buying process. However, a person who’s earlier in the buying process might have more appreciation for a sales sheet with a bulleted list of product advantages.
4. Improving Customer Service Methods and Impacts
Poor or average customer service could cause a B2B customer to give up and do business with another provider. Conversely, excellent customer service increases the likelihood of an entity being a long-term client that increases a B2B company’s profits. This could lead to more customers due to positive word-of-mouth.
A 2018 Accenture report found that 79% of B2B companies use chatbots for customer service, and 73% depend on virtual reality (VR) or augmented reality (AR) for customer service purposes. Those statistics indicate that B2B companies are increasingly on board with high-tech options for addressing customer needs.
B2B companies can increase the worthiness of their customer service efforts even more by using big data platforms and doing it in conjunction with some of the technologies mentioned in the Accenture research. For example, a B2B company could invest in a big data tool to analyze customer sentiment in conversations with a chatbot.
In that scenario, the data analysis platform would pick up on specific words that likely indicate extreme frustration. Then, a company representative could be notified of the customer’s situation and take over the conversation to try and make things right.
Alternatively, if a B2B company has multiple customer service channels — such as telephone support, live chat and email — big data analytics can indicate which methods are used the most during certain days or times. Such insights could improve decision-making, such as helping a company determine whether to extend its phone support hours or hire another person to respond to email questions.
Big Data Is Relevant to B2B
The examples on this list highlight why B2B professionals should take big data seriously. The insights they receive from analytics tools could positively alter how they operate.
